LARA: Latency-Aware Resource Allocator for Stream Processing Applications

At the 32nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2024), we presented LARA, a tool that improves latency in stream processing applications, a crucial requirement for real-time video analytics. By using a regression-based resource allocation technique, LARA improves latency by up to 2.8X and delivers over 2X throughput compared to fixed allocation, surpassing the vertical pod autoscaler (VPA) in performance.

Improving Real-time Data Streams Performance on Autonomous Surface Vehicles using DataX

Our paper, presented at PDP 2024, discusses a containerized distributed processing platform for Autonomous Surface Vehicles to enhance real-time data processing in marine environments. Utilizing microservice management with DataX and Kubernetes, it addresses challenges such as limited connectivity and energy constraints. Experiments demonstrate its effectiveness in marine litter detection.

Scale Up while Scaling Out Microservices in Video Analytics Pipelines

Our paper, presented at POAT 2023 in Singapore, examines joint microservice scaling in Kubernetes, focusing on video analytics pipelines. It introduces DataX AutoScaleUp, which efficiently adjusts CPU resources while Horizontal Pod Autoscaler (HPA) operates. This method significantly enhances processing rates, achieving up to 1.45X improvement over traditional approaches.

AnB: Application-In-A-Box To Rapidly Deploy and Self-Optimize 5G Apps

Our Application in a Box (AnB) project, presented at SMARTCOMP 2023, simplifies the deployment of remote 5G applications. AnB includes pre-configured hardware and software, allowing quick setup without extensive technical knowledge. It features automated resource management for optimized performance, demonstrating real-world applications and significantly reducing deployment time from months to minutes.

Content-aware auto-scaling of stream processing applications on container orchestration platforms

At the PDP 2023 conference, we presented a study on application scaling in microservices deployed via Kubernetes. It critiques the Horizontal Pod Autoscaler (HPA) for inefficient scaling due to neglecting microservice interactions. The proposed DataX AutoScaler improves performance by accounting for these interactions, achieving up to 43% better performance in video analytics applications.

DataX Allocator: Dynamic resource management for stream analytics at the Edge

At the 9th International Conference on IOTSMS 2022, we presented a reinforcement-learning technique to enhance serverless edge computing by optimizing resource allocation for microservices. This innovative approach achieved remarkable improvements in processing rate in real-world applications, demonstrating versatility and efficiency that promise to revolutionize AI and machine learning.